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math-olympiad

"Solve competition math problems (IMO, Putnam, USAMO, AIME) with adversarial verification that catches the errors self-verification misses.

Install / Use

npx skills add anthropics/claude-plugins-official --skill math-olympiad

Installs into whichever agent you are using.

About this skill
📄

SKILL.md

Installable skill definition

Quality Score

99/100

Supported Platforms

Universal

Tags

Our assessment of math-olympiad

math-olympiad scores 99/100 on our quality scale, 47th of 1,753 Development & Engineering skills we index (top 3%).

Its SKILL.md is 19 KB long, well organised into 19 sections with 6 code examples: a thorough specification that gives an agent plenty to work with.

With 36,726 GitHub stars, it is one of the more widely adopted skills in the catalogue.

Substance
30/30
Structure
20/20
Description
15/15
Adoption
19/20
Freshness
15/15

Maintenance, license and trust

  • The repository was last updated today, so math-olympiad is actively maintained.
  • It is released under the Apache-2.0 license, a permissive license that allows use, modification and commercial use with attribution.
  • Its trust signals score 100/100, with no cautions. These come from repository metadata, not a code audit — read the skill file before letting an agent act on it.

Safety scan

No issues found

Our scan of the whole file found no instruction hijacking, hidden characters, credential access, data exfiltration or destructive commands. An AI review of the same text found nothing harmful.

AI review by kimi-k2.7-code on 2026-09-25. Automated pattern scan on 2026-09-25. It catches known dangerous patterns, not every risk — read a skill before letting an agent act on it.

math-olympiad compared with similar skills

All 4 of these similar skills score higher than math-olympiad; compare them before choosing.

SkillScoreStarsUpdatedFormat
math-olympiad (this skill)by anthropics9936.7ktodaySKILL.md
ai-job-searchby MadsLorentzen10043.9k4d agoCLAUDE.md
claude-howtoby luongnv8910041.7k5d agoCLAUDE.md
algorithmic-artby anthropics100177.9k2d agoSKILL.md
pptxby anthropics100177.9k2d agoSKILL.md

Frequently asked questions

How do I install math-olympiad?
Run npx skills add anthropics/claude-plugins-official --skill math-olympiad. The install tabs above show the steps for each supported agent.
Which AI agents does math-olympiad work with?
It is written for Universal, as a SKILL.md file. Other agents that read the same format can often use it too.
Is math-olympiad safe to use?
Our scan of the whole file found no instruction hijacking, hidden characters, credential access, data exfiltration or destructive commands. An AI review of the same text found nothing harmful. It is Apache-2.0-licensed and scores 100/100 on trust signals. Skills are instructions an agent will follow, so read the file before installing it and do not approve commands you do not understand.
Is math-olympiad still maintained?
The repository was last updated today, so math-olympiad is actively maintained.

name: math-olympiad description: "Solve competition math problems (IMO, Putnam, USAMO, AIME) with adversarial verification that catches the errors self-verification misses. Activates when asked to 'solve this IMO problem', 'prove this olympiad inequality', 'verify this competition proof', 'find a counterexample', 'is this proof correct', or for any problem with 'IMO', 'Putnam', 'USAMO', 'olympiad', or 'competition math' in it. Uses pure reasoning (no tools) — then a fresh-context adversarial verifier attacks the proof using specific failure patterns, not generic 'check logic'. Outputs calibrated confidence — will say 'no confident solution' rather than bluff. If LaTeX is available, produces a clean PDF after verification passes." version: 0.1.0

Math Olympiad Solver

The five things that change outcomes

  1. Strip thinking before verifying — a verifier that sees the reasoning is biased toward agreement. Fresh context, cleaned proof only.
  2. "Does this prove RH?" — if your theorem's specialization to ζ is a famous open problem, you have a gap. Most reliable red flag.
  3. Short proof → extract the general lemma — try 2×2 counterexamples. If general form is false, find what's special about THIS instance.
  4. Same gap twice → step back — the case split may be obscuring a unified argument. Three lines sometimes does what twelve pages couldn't.
  5. Say "no confident solution" — wrong-and-confident is worse than honest abstain.

Tool policy: Solvers and verifiers use THINKING ONLY in the tight-budget workflow. Competition math is reasoning. Computation is for deep mode (§6c), and even then bounded — a recurrence that's doubly-exponential can't be computed past n~30, work mod 2^m instead.


When to use which approach

| Problem | Approach | Verification | | ---------------------------------------------------- | ------------------------------------------------------------------------------ | ------------------------- | | AIME numeric answer | Best-of-N → majority vote | Answer check only | | Olympiad proof (IMO/Putnam/USAMO) | Full workflow below | 5-pass adversarial | | "Is this proof correct?" | Skip to verification (step 4) | Adversarial + spec-gaming | | Full problem set (e.g. all 6 from a competition) | Sequential: one full workflow per problem, collect results, compile single PDF | Per-problem adversarial |

Batch in one Workflow: Set opts.label on every agent() call to include the problem ID (e.g., label: "P3:solver:2"). Without labels, 36 results come back with no problem association. Run problems in parallel — the label is what matters, not ordering.

For a full problem set

Launch one solver workflow per problem (same VERBATIM prompt, different statement). Run them in parallel. When all return, run adversarial verification per problem. Problems that pass get their proof in the PDF; problems that abstain get "No confident solution" with partial notes.

Don't try to solve all N problems in one agent's context — each problem needs its own thinking budget and its own fresh-context verifier. The composition is mechanical: collect the per-problem outputs, fill in LaTeX sections, compile once. | "Simplify this proof" | Skip to presentation (step 8) | — |


The Workflow

1. Interpretation check (30 seconds, catches 50/63 of one class of errors)

Before solving anything, identify the interpretation.

Read the problem statement. List 2-3 ways it could be interpreted. For each: is this reading TRIVIAL? If one reading makes the problem easy and another makes it hard, the hard one is almost certainly intended. State which interpretation you're solving and WHY you believe it's the intended one.

The Aletheia case study found 50 of 63 "technically correct" solutions were for the wrong interpretation. Olympiad problems often have a trap easy reading.

2. Generate candidates with internal refinement (parallel, thinking only)

Launch 8-12 attempt agents in parallel. Each agent internally iterates — solve → self-improve → self-verify → correct → repeat. This is the Yang-Huang structure that achieves 85.7% on IMO: one-shot solving isn't enough; per-attempt refinement matters.

The Agent tool cannot enforce tool restriction. Subagents get the full tool set. The only mechanism is the prompt. Use this prompt VERBATIM — do not summarize, do not synthesize your own:

NO COMPUTATION. Do not use Bash, Python, WebSearch, Read, Write, or any tool that runs code or fetches data. Numerical verification is not a proof step. "I computed n=1..10 and the pattern holds" is not a proof.

(If your agent harness requires a StructuredOutput or similar return-mechanism tool call, that is NOT a computation tool — call it to return your answer. The restriction is on tools that DO work, not tools that REPORT work.)

Your internal process (iterate until done):
- Solve: Complete rigorous solution.
- Self-improve: Reread. Fix gaps before a grader sees it.
- Self-verify: Strict grader mode. Every step justified?
- Correct: Fix and re-verify. Up to 5 rounds.
- Stop: Self-verify passes twice clean, OR 5 rounds, OR approach fundamentally wrong.

A correct answer from flawed reasoning is a failure. If incomplete, say so honestly. Never hide gaps.

PROBLEM: <insert the problem statement here>
ANGLE: <insert one starting angle here>

The first two paragraphs are load-bearing. A session that writes its own prompt and omits them will produce subagents that grind Python for 30 iterations and confidently get wrong answers — a pattern that fits n≤10 but fails at n=100 is not a proof.

Starting angles (vary across agents — see references/solver_heuristics.md):

  • Work out small cases (test past n=3)
  • Look for an invariant or monovariant
  • Consider the extremal case
  • Try induction
  • What symmetries?
  • Work backwards
  • Drop a condition — where does it become trivially false?
  • Generalize (inventor's paradox — more structure is sometimes easier)

Each returns its FINAL state (not intermediate rounds):

**Verdict**: complete solution | partial result | no progress
**Rounds**: [how many verify→correct cycles]
**Method**: [key idea, one paragraph]
**Detailed Solution**: [full step-by-step, every step justified]
**Answer**: [if applicable]
**Self-verification notes**: [what you caught and fixed; remaining concerns]

Retry policy: If an agent fails or times out, retry once. Transient failures happen.

3. Clean the solution (context isolation — the #1 lever)

The thinking trace biases the verifier toward agreement — a long chain of reasoning reads as supporting evidence even when the conclusion is wrong. Before any verification, strip:

  • All thinking-block content
  • All "Let me try..." / "Actually wait..." / "Hmm" prose
  • All false starts and backtracking

What remains: problem statement + clean final argument only.

Extract only the Method + Proof + Answer sections from each solver's output. The verifier never sees how the solver got there.

4. Adversarial verify (fresh context, pattern-armed)

For each cleaned solution, launch a fresh verifier agent. Fresh context: it sees only (problem statement + cleaned solution). No tools.

The verifier's job is to ATTACK, not grade. Load references/adversarial_prompts.md for the prompts. The key patterns it runs:

| Pattern | The check | | ------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------ | | #4 | Does this theorem specialize to a famous object (ζ, quadratic reciprocity, etc.) and prove something open about it? → gap | | #18 | Substitute the proof's own intermediate identities into any "remaining gap." Recover the original claim? → tautological | | #40 | Is any step a "one-line lemma"? Extract the GENERAL form. Find a 2×2 counterexample. If the general form is false, find what special structure saves THIS instance | | #5 | For each invoked theorem: re-check hypotheses FROM SCRATCH. "Continuous on [0,1]" ≠ "continuous on ℝ" | | #6 | Any infinite sum "bounded" via a regularized value? Check the boundary — if there's a pole there, the sum diverges |

Full pattern list: references/verifier_patterns.md

Verifier returns:

**Verdict**: HOLDS | HOLE FOUND | UNCLEAR

**If HOLE FOUND**:
- Location: [quote the problematic step]
- Pattern: [which check fired, or "other"]
- Why it breaks: [specific]
- Fixable?: [yes with X / no, fundamental]

5. Rank and vote-verify (asymmetric + early exit)

Rank solutions by (verdict, verifier confidence). Take the top one. Run up to 5 fresh verifier agents.

Asymmetric thresholds: 4 HOLDS to confirm, 2 HOLE FOUND to refute. Why asymmetric: one flaky verifier shouldn't kill a correct proof; but two independent dissents is a real signal.

Pigeonhole early exit: stop launching verifiers once the outcome is decided.

  • 2 say HOLE FOUND → refuted, stop (save the remaining 3 calls)
  • 4 say HOLDS → confirmed, stop (save the 5th)
  • After 3 verifiers: if 2 HOLDS + 1 HOLE, launch 2 more (outcome undecided). If 3 HOLDS + 0 HOLE, launch 1 more (could still hit 4-1).

Dual context-isolation: each verifier is blind to (a) the solver's thinking trace — already stripped in step 3 — AND (b) other verifiers' verdicts. Each verifier thinks it's the first. No "3 agents already confirmed this" social proof.

A solver cannot verify its own solution. Different agent, fresh context.

5b. When one case won't close — step back before grinding

If a proof splits into cases and one case proves easily but the other resists: before grinding through the hard case, ask whether there's a route that makes the split disappear.

The pattern that saves you: the hard case's very hypothesis often implies something strong about an intermediate object you haven't looked at. Use that implication directly instead of the original chain.

Concrete shape: proving f(n) ≤ cn for a constrained function f, with a case split on a prime p dividing f(n). One branch closes by index arguments in (ℤ/p^e)*. The other branch resists — same group structure, but the arithmetic doesn't contradict. The fix: the hypothesis "p | f(n)" plugged back into the governing equation implies f(p) = p itself. Once you have that, a Fermat+Dirichlet argument kills both branches in three lines. The case split was a detour — it was splitting on a variable that, under the hypothesis, takes a known value.

Check when stuck on case B:

  • What does case B's hypothesis imply about f at other inputs?
  • Is there a different pair (a,b) to plug into the governing equation?
  • Are you proving too much? (A cleaner contradiction needs less machinery.)

This is also a presentation-pass win: the split-free proof is shorter AND more general.

6. Revise (if needed)

If verification finds a hole: launch a reviser agent. It gets (cleaned solution + verifier's hole report). STILL no access to the original thinking — the reviser works from the hole, not by rereading how you got there.

A verifier found this issue in the proof:
[hole report]

Fix the proof. If the hole is 

Truncated for display — read the full file on GitHub.

Related Skills

View on GitHub
GitHub Stars36.7k
CategoryDevelopment
Updated9h ago
Forks4.1k

Languages

Python

Trust signals

100/100

From repository metadata: license, adoption, age and documentation. Not a code audit — see the Safety scan above for what the skill file itself contains.

No cautions